In their cross-sectional study, Al-Rubaye et al studied the extent of vitamin D (VD) deficiency/ insufficiency, factors affecting its degree, and the adverse outcomes of the altered VD status among a group of mothers and their neonates from Baghdad, Iraq. They found that 96.6% of the mothers had VD deficiency/ insufficiency compared to 86.4% in their neonates. Maternal VD levels and neonatal weights were significantly correlated with neonatal VD levels. We believe that the study results need to be revised. This is based  two points listed in this letter to editor

Background: Vitamin D deficiency/ insufficiency is common in different age groups in both genders especially among pregnant women and neonates where it is associated with several adverse outcomes including preeclampsia and preterm delivery.  

Objectives: To assess the extent of vitamin D deficiency/ insufficiency among mothers and their neonates and some factors related to it and identify some adverse outcomes of the deficiency/ insufficiency on neonates (preterm birth and low birth weight).

Subject and Methods: A cross-sectional study was conducted on 88 Iraqi pregnant women and neonates admitted to “Al-Elwiya teaching hospital for maternity” in Baghdad- Al

Aim: To evaluation the effect of Lactobacillus acidophilus on Enterohemorrhagic Escherichia coli (EHEC) serotype O157:H7 with detection of some virulence factors. Methods: Two hundred and fifty specimens (stool) from children under five years for both sexes were collected from some hospitals. All isolates were diagnosed according to morphological characteristics, biochemical tests. Monoplex pattern of PCR was used also for detection different genes in (7) Escherichia coli )O157:H7 (isolates; include 16SrRNA, eae, lifA, Stx1,Stx2 that encoded for ribosomal RNA, intimin, lymphocyte inhibitory factor, shiga toxins. Three types of probiotics strains were obtained, Lactobacillus fermentum, Lactobacillus plantarum and Lactobacillus acidophilus (A

Introduction The Hybrid Gamma Camera (HGC) is being developed to enhance the localisation of radiopharmaceutical uptake in targeted tissues during surgical procedures such as sentinel lymph node (SLN) biopsy. Purpose To assess the capability of the HGC, a lymph-node-contrast (LNC) phantom was constructed for an evaluative study simulating medical scenarios of varying radioactivity concentration and SLN size. Materials and methods The phantom was constructed using two methyl methacrylate PMMA plates (8 mm thick). The SLNs were simulated by drilling circular wells of diameters ranging between 10 mm and 2.5 mm (16 wells in total) in one plate. These simulated SLNs were placed underneath scattering material with thicknesses ranging between 5 mm

Abstract

Antibiotic treatment of  S.typhi is difficult as compared to treatment of acute infection. Antibiotic resistance carried against S.typhi  by using 6 kinds of antibiotics from  different classes, their results  showed that all isolates were  high resistance to Ampicillin (99%), Gentamicin (98%), Amikacin (79%) and   less  resistances  Trimethoprim (55%) ,  Imipenem (60%)  and Ceftriaxone(66%)  .

The present study focused on the molecular detection of Wzx flippase, Wzy polymerase genes in some Salmonella typhi isolates, Samples were collected from typhoid patients by classical lab work. Antibiotics susceptibilit

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat